Unlike most statistics courses which focus in the inferential side of models, Max's talk instead focuses on creating statistical models where the goal is prediction. Given his background at Pfizer, the talk includes analysis some interesting datasets, including one to predict the performance an algorithm used to identify cell components (the cell wall, nucleus, etc) in a microscopy slide. (The data is public, so you can recreate the analyses using this R code and the caret package.)

In addition to an overview of predictive modeling in general, you'll learn how to use R to use resampling techniques to select tuning parameters in a model (for example, the number of trees to use in a random forest), and how to evaluate the performance for classification using the confusion matriX and ROC curves. Watch the presentation below:

If you find the slides a little small to read, you can download them here and follow along.